https://hub.docker.com/r/jupyter/scipy-notebook/
$ docker pull jupyter/scipy-notebook
$ docker run -v `pwd`:/home/jovyan/work -p 10000:8888 --name jupyter jupyter/scipy-notebook
...
To access the server, open this file in a browser:
package io.trino.operator.scalar; | |
import com.google.common.collect.ImmutableList; | |
import io.airlift.slice.Slice; | |
import io.airlift.slice.Slices; | |
import io.trino.annotation.UsedByGeneratedCode; | |
import io.trino.metadata.SqlScalarFunction; | |
import io.trino.spi.block.Block; | |
import io.trino.spi.block.SqlRow; | |
import io.trino.spi.function.BoundSignature; |
https://hub.docker.com/r/jupyter/scipy-notebook/
$ docker pull jupyter/scipy-notebook
$ docker run -v `pwd`:/home/jovyan/work -p 10000:8888 --name jupyter jupyter/scipy-notebook
...
To access the server, open this file in a browser:
require 'trino-client' | |
require 'tiny-presto' | |
client = Trino::Client.new(server: 'localhost:8080', catalog: 'memory', user: 'test-user', schema: 'default') | |
cluster = TinyPresto::Cluster.new('trinodb/trino', '355') | |
container = cluster.run | |
loop do | |
begin | |
# Make sure to all workers are available. | |
client.run('show schemas') |
name: build | |
on: [push, pull_request] | |
jobs: | |
build: | |
runs-on: ubuntu-latest | |
strategy: | |
matrix: | |
java: [8, 11] |
trait CyclicA { | |
val b = bind[CyclicB] | |
} | |
trait CyclicB { | |
val a = bind[CyclicA] | |
} | |
val d = Design.newDesign | |
d.build[CyclicA] { a => |
Start event server:
$ pio eventserver &
diff --git a/swagger/src/main/scala/org/scalatra/swagger/Swagger.scala b/swagger/src/main/scala/org/scalatra/swagger/Swagger.scala | |
index 97e00e46..f5525ca2 100644 | |
--- a/swagger/src/main/scala/org/scalatra/swagger/Swagger.scala | |
+++ b/swagger/src/main/scala/org/scalatra/swagger/Swagger.scala | |
@@ -45,16 +45,6 @@ trait SwaggerEngine[T <: SwaggerApi[_]] { | |
object Swagger { | |
- val excludes: Set[java.lang.reflect.Type] = Set( | |
- classOf[java.util.TimeZone], |
Test data:
{"maker":"Apple", "products":[{"name": "iPhobe", "price": 100000}, {"name": "iPad", "price": 120000}]}
{"maker":"ASUS", "products":[{"name": "Zenfone", "price": 20000}]}
DataFrame:
scala> val df = ds.select(ds("maker"),explode(ds("products")).as("p"))
scala> df.select("maker", "p.name", "p.price").show